Due to their open nature and popularity, Android-based devices represent one of the main targets for malware attacks that may adversely affect the privacy of their users. Considering the huge Android ...market share, it is necessary to build effective tools able to reliably detect zero-day malware on these platforms. Therefore, several static and dynamic analysis methods based on Neural Networks and Deep Learning have been proposed in the literature. Despite machine learning can be considered the most promising approach for classifying applications into malware or legitimate ones, its success strongly depends on the choice of the right features used for building the detection model. This is definitely not an easy task that requires a systematic solution. Accordingly, this work represents the sequences of API calls invoked by apps during their execution as sparse matrices looking like images (API-images), which can be used as fingerprints of the apps’ behavior over time. We also used autoencoders to autonomously extract the most representative and discriminating features from these matrices, that, once provided to an artificial neural network-based classifier have shown to be effective in detecting malware, also when the network is trained on a reduced number of samples. Experimental results show that the resulting framework is able to outperform more complex and sophisticated machine learning approaches in malware classification.
•Using Neural Networks and Deep Learning to timely detect zero-day malware on Android platforms.•Building applications’ classification model needed for flagging privacy/security breaches.•Modeling application behavior through sparse matrices representing API calls’ sequences (API-Images).•Using autoencoders to autonomously extract the most representative and discriminating features.•Performing classification and detection based on softmax multi-class regressors.
•An SNA based conflict detection and elimination decision making process is presented.•The impact of relationship strength on trust propagation efficiency is considered.•Multi-path trust propagation ...operator is presented to complete the social network.•Nonlinear optimization model guarantees a sufficient reduction of group conflict.•We promote the modification of the assessments by finding the optimal solution.
The paper proposes a Trust Relationship-based Conflict Detection and Elimination decision making (TR-CDE) model, applicable for Large-scale Group Decision Making (LSGDM) problems in social network contexts. The TR-CDE model comprises three processes: a trust propagation process; a conflict detection and elimination process; and a selection process. In the first process, we propose a new relationship strength-based trust propagation operator, which allows to construct a complete social network by considering the impact of relationship strength on propagation efficiency. In the second process, we define the concept of conflict degree and quantify the collective conflict degree by combining the assessment information and trust relationships among decision makers in the large group. We use social network analysis and a nonlinear optimization model to detect and eliminate conflicts among decision makers. By finding the optimal solution to the proposed nonlinear optimization model, we promote the modification of the assessments from the DM who exhibits the highest degree of conflict in the process, as well as guaranteeing that a sufficient reduction of the group conflict degree is achieved. In the third and last process, we propose a new selection method for LSGDM that determines decision makers’ weights based on their conflict degree. A numerical example and a practical scenario are implemented to show the feasibility of the proposed TR-CDE model.
•A comprehensive review on the integration of machine learning into meta-heuristics.•A unified taxonomy to provide a common terminology and classification.•Classification of numerous articles based ...on different characteristics.•Technical discussions on advantages, limitations, requirements, and challenges.•Proposition of promising future research directions.
In recent years, there has been a growing research interest in integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. This integration aims to lead meta-heuristics toward an efficient, effective, and robust search and improve their performance in terms of solution quality, convergence rate, and robustness.
Since various integration methods with different purposes have been developed, there is a need to review the recent advances in using machine learning techniques to improve meta-heuristics. To the best of our knowledge, the literature is deprived of having a comprehensive yet technical review. To fill this gap, this paper provides such a review on the use of machine learning techniques in the design of different elements of meta-heuristics for different purposes including algorithm selection, fitness evaluation, initialization, evolution, parameter setting, and cooperation. First, we describe the key concepts and preliminaries of each of these ways of integration. Then, the recent advances in each way of integration are reviewed and classified based on a proposed unified taxonomy. Finally, we provide a technical discussion on the advantages, limitations, requirements, and challenges of implementing each of these integration ways, followed by promising future research directions.
Smartphones have become pervasive due to the availability of office applications, Internet, games, vehicle guidance using location-based services apart from conventional services such as voice calls, ...SMSes, and multimedia services. Android devices have gained huge market share due to the open architecture of Android and the popularity of its application programming interface (APIs) in the developer community. Increased popularity of the Android devices and associated monetary benefits attracted the malware developers, resulting in big rise of the Android malware apps between 2010 and 2014. Academic researchers and commercial antimalware companies have realized that the conventional signature-based and static analysis methods are vulnerable. In particular, the prevalent stealth techniques, such as encryption, code transformation, and environment-aware approaches, are capable of generating variants of known malware. This has led to the use of behavior-, anomaly-, and dynamic-analysis-based methods. Since a single approach may be ineffective against the advanced techniques, multiple complementary approaches can be used in tandem for effective malware detection. The existing reviews extensively cover the smartphone OS security. However, we believe that the security of Android, with particular focus on malware growth, study of antianalysis techniques, and existing detection methodologies, needs an extensive coverage. In this survey, we discuss the Android security enforcement mechanisms, threats to the existing security enforcements and related issues, malware growth timeline between 2010 and 2014, and stealth techniques employed by the malware authors, in addition to the existing detection methods. This review gives an insight into the strengths and shortcomings of the known research methodologies and provides a platform, to the researchers and practitioners, toward proposing the next-generation Android security, analysis, and malware detection techniques.
Synchronization is a very important phenomenon in the nervous system, which is closely related to the encoding, integration and transmission of information. In this paper, synchronization and ...transition of a two-compartment respiratory neuron model under transcranial magnetic stimulation (TMS) are studied from the perspective of synchronization degree for the first time. We are established the correlation degree with synchronization, and discussed the firing mode and transition rule of the neurons in the two-compartment compartment pre-Bötzinger complex (PBC) by means of bifurcation theory and Lyapunov index. The results show that the synchronization of neurons has a great influence under TMS, which is embodied in the fact that the somatic will experience a peak firing and a transition from bursting to resting under the magnetic stimulation,which was a phenomenon never before shown in PBC neurons. These results fully reveal the dynamic behavior of PBC nervous system under TMS, and provide theoretical value for further understanding of respiratory rhythm.
•The dynamic behavior of PBC under TMS was studied.•The degree of synchronization was first used to study the respiratory rhythm of PBC.•Abundant bifurcation phenomenon is obtained.
With the rapid development of remote sensing technology, the monitoring of land surface ecological status (LSES) based on remote sensing has made remarkable progress, which has a positive ...contribution on improving the regional ecological environment and promoting the realization of Sustainable Development Goals (SDGs). Among them, the proposed Remote Sensing-based Ecological Index (RSEI) becomes the most widely used model in the current application of remote sensing-based LSES monitoring due to its complete derived from remote sensing images and no subjective intervention. RSEI is not flawless either, and it still suffers from some uncertainties in its application in multiple scenarios. However, compared to the extensive applied research, work on the instability assessment and improvement of RSEI is particularly scarce and urgently needed. Therefore, in this paper, we analyzed the possible instabilities in the RSEI calculation process and proposed various inversion models to evaluate their accuracy and stability in time-series LSES monitoring. The results indicated that the existing normalized RSEI is relatively stable for the characterization of single-phase LSES, however, there is a high risk in the time-series analysis or cross-regional comparison due to the interference of component extremes. The standard deviation discretized DRSEIs proposed in this paper perform better in both single-phase and long-term dynamics LSES assessments and are more consistent with the real land cover changes. Also, compared with the approach that measures LSES dynamics using time-series regional RSEI mean values, the DRSEIs change detection results can reveal the spatial heterogeneity of regional LSES dynamics more effectively and provide a finer reference for the formulation and implementation of ecological protection policies.
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•The applicability of normalization and standardization in RSEI is well discussed.•The instability in the RSEI time-series assessment was found and replicated.•A discrete RSEI (DRSEIs) is proposed, which is more suitable for dynamic analysis.
Grey-box modeling, as one of the three fundamental modeling techniques for building energy models, has many advantages compared with black-box modeling and white-box modeling. It has been widely ...applied to solve problems of building technologies, such as building load estimation, control and optimization, and building-grid integration. However, a thorough review of grey-box modeling is not available. This review study systematically investigated various aspects of grey-box modeling for buildings. First, the fundamental aspects of grey-box modeling are presented, including the theoretical background, modeling of building elements, modeling order, modeling diagram, and order reduction. Second, the detailed modeling approaches are discussed. Third, multiple applications of grey-box modeling are investigated for building energy domain, which are categorized into the following groups: heat dynamics analysis, thermal load estimation, building control and optimization, district/urban scale energy modeling, and building-grid integration. Finally, the available software packages for grey-box modeling are compared. Overall, the challenges of using grey-box modeling can be summarized as follows: (1) the theoretical limitations and assumptions of grey-box modeling are unclear; (2) grey-box model naming convention and structure are confusing; (3) grey-box model creation is vague; (4) suitable applications of grey-box models are unknown; and (5) grey-box models lack unified software solutions for wider adoption.
•Grey-box modeling has many advantages over white-box and black-box modeling.•Naming convention are confusing for grey-box modeling structure, regarding the envelope, zone, or whole building.•There are two approaches to create a grey-box model: forward and inverse (data-driven).•Grey-box modeling is suitable for other applications, other than thermal load or control.•Grey-box modeling has no unified software solution.
•A nonlinear dynamic model is proposed to predict the skidding behavior.•Various effects are considered for roller bearings under time-variable load.•Local skidding for each roller could still be ...found and vary periodically.•Time-variable load lead to the frequency of time-varying slipping velocity change.•The results are useful for design and monitoring of rotating machinery.
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Considering the radial clearance, roller crown profile and discontinuous contact between the roller and cage, a nonlinear dynamic model for skidding behavior of the cylindrical roller bearing is established based upon the Hertz contact theory and elastohydrodynamic lubrication. Through comparisons with both reference and experimental results, the proposed model is verified. Various load conditions are considered for their effects on single roller’s skidding behavior. It is shown that local skids exist and periodically vary with the roller revolution, especially for the roller entering and leaving the load region. Increasing the values of radial load, bending moment or amplitude of time-variable load all reduces the maximum roller slipping velocity, which means that the roller skids are attenuated. After considering the time-variable radial load, the frequency of time-varying slipping velocity is not the orbital speed but the combination of the inner race frequency and orbital speed.
•We present new operations of the probabilistic linguistic term sets.•We build a correlation measure-based consensus reaching process.•We propose a new outranking method: gained and lost dominance ...score method.•We give a probabilistic linguistic gained and lost dominance score method.•We validate the method with the green enterprise selection problem.
This paper proposes a comprehensive Multiple Criteria Group Decision Making (MCGDM) method with probabilistic linguistic information based on a new consensus measure and a novel outranking method, Gained and Lost Dominance Score (GLDS). Firstly, new operations of the probabilistic linguistic term sets are introduced based on the adjusted rules of probabilistic linguistic term sets and the linguistic scale functions for semantics of linguistic terms. After defining a new consensus measure based on the correlation degree between probabilistic linguistic term sets, we develop a consensus reaching method to improve the consensus degree of a group. To rank alternatives reasonably, we further propose the GLDS method which considers both the “group utility” and the “individual regret” values. The core of the GLDS is to calculate the gained and lost dominance scores that the optimal solution dominates all other alternatives in terms of the net gained dominance flow and the net lost dominance flow. Then, we integrate the GLDS ranking method with the consensus reaching process and develop a consensus-based PL-GLDS method to solve the MCGDM problems with probabilistic linguistic information. Finally, the proposed method is validated by a case study of selecting optimal green enterprises. Some comparative analyses are given to show the efficiency of the proposed method.
This Special Issue reprint is dedicated to presenting open and challenging issues in earthquake engineering. It consists of 29 peer-reviewed papers that cover a broad range of subjects and ...applications related to the seismic assessment and design of structures. Based on advanced computational, analytical, numerical, and experimental approaches novel results and discussions are presented. Within this context, the first studies of this issue/reprint are focused on providing an insight into the seismic performance of structures taking into account significant engineering components that still have not been fully addressed. Subsequently, there are studies with new strategies to improve the effectiveness of the dampers on the seismic mitigation performance of structures. Concerning performance-based earthquake engineering, new approaches in the seismic fragility assessment of structures are introduced. Furthermore, new innovative types of reinforced steel for the seismic design and assessment of RC structures are analytically and experimentally evaluated. The seismic performance of retrofitted structures is also addressed, while analytical modeling tools that can effectively capture the seismic behavior of substandard RC structural elements are introduced. Some other papers provide experimental results to evaluate and/or validate the structural performance of elements, such as anchors, connectors, and nuclear components. Finally, this issue/reprint also incorporates modified methodologies and identification techniques to improve seismic analysis methods in the field of structural engineering.
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